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Speaker:        Dr. James Gee
                Department of Radiology
                University of Pennsylvania
                U.S.A.

Topic:          "Information Processing in Medical Imaging:
                 Computational Anatomy"

Date:           Friday, 20 September, 2002

Time:           11:00 am - 12 noon

Venue:          Room 3401 (Phase I, via lift nos. 17/18)
                HKUST

Abstract:

Medical imaging modalities routinely provide a variety of
information---ranging from highly detailed, three-dimensional pictures of
structural anatomy to maps of functional activity within the body---that
has become indispensable in investigating the health of an individual. In
addition to its well-established clinical role in facilitating diagnosis
and in monitoring response to treatment, imaging has gained an equally
important function in advancing basic research in many areas of the
biomedical sciences.  A central factor in the success and increasingly
wide-spread application of imaging-based approaches in medicine has been
the emergence of sophisticated computational methods that utilize external
knowledge in the form of geometric, statistical and/or
domain/modality-specific models for extracting clinically significant and
scientifically important information from image data.  This talk aims to
highlight some research directions in model-based methods for processing
and analysis of medical images by discussing the quantitation of
anatomical structure and its application in morphometric and biomechanical
studies, respectively, of the human brain and lung.


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Biography:

James Gee, Ph.D., is Assistant Professor of Radiologic Science in the
Department of Radiology at the University of Pennsylvania School of
Medicine.  He holds B.S. degrees in Computer Science and Electrical
Engineering, an M.S. in Electrical Engineering, all from the University of
Washington, and a Ph.D. in Computer and Information Science from the
University of Pennsylvania.  His research interests include biomedical
imaging, probabilistic and geometric modeling, pattern analysis, and
scientific computing. Best known for contributions to non-rigid image
registration, his group has pioneered the application of Bayesian modeling
and inference to problems in registration and morphometry as well as the
use of finite element techniques for numerical implementation of the
solutions.


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